Innovation efficiency of Chinese provincial high-tech industries based on shared feedback DEA model

ZHU Yu1,2 YANG Feng2 JIANG Li-jing2 LIU Pei2

(1.School of Management Engineering, Anhui Polytechnic University, Wuhu, Anhui Province, China 241000)
(2.School of Management, University of Science and Technology of China, Hefei, Anhui Province, China 230026)

【Abstract】Data envelopment analysis (DEA) has been proved to be an excellent approach for measuring of innovation performance of high-tech industries, but the existing literature ignores that enterprises will return the economic benefits of innovation to two stages for further development and production, so as to ensure continuous innovation. Therefore, this paper combines the characteristics of the innovation process of high-tech industries into two stages of technology R&D and commercial transformation. A two-stage efficiency measuring model considering shared feedback is proposed, which not only extends the DEA methods, but also promotes the research on innovation performance management. The empirical results show that the overall efficiency of Chinese high-tech industries is good, though there is still room for improvement. However, the regional development is unbalanced, and there are obvious regional differences in the efficiency scores in different stages. The implementation of targeted management is an effective measure to improve the innovation performance.

【Keywords】 high-tech industries; data envelopment analysis(DEA); shared feedback; two-stage; innovation efficiency;

【DOI】

【Funds】 Philosophy and Social Science Fund for Young Scholars of Anhui Province, China (AHSKQ2015D51)

Download this article

    References

    [1] Chen K H, Guan J C, Kou M T. The cruxes and countermeasures of China’s high-tech industries“high outcomes, low benefits”: An empirical investigation based on the productive efficiency analysis of technological innovation activities [J]. Management Review, 2012, 24 (4): 55–68 (in Chinese).

    [2] Feng Z J, Chen W. R&D innovation efficiency of Chinese high-tech industries—Based on two-stage network DEA model with constrained resources [J]. Systems Engineering—Theory & Practice, 2014, 34 (5): 1202–1212 (in Chinese).

    [3] Bai J H, Jiang F X. Synergy innovation, spatial correlation and regional innovation performance [J]. Economic Research Journal, 2015, 7: 174–187 (in Chinese).

    [4] Hollanders H, Celikel-Esser F. Measuring innovation efficiency: INNO-Metrics thematic paper [C]. Brussels: European Commission, DG Enterprise, 2007: 1–27.

    [5] Xiao R Q, Chen Z W, Qian L. China’s high-tech manufacturing industries’ innovation efficiency: Technology heterogeneity perspective [J]. Journal of Management Science, 2018, 31 (1): 48–68 (in Chinese).

    [6] Charnes A, Cooper W W, Rhodes E. Measuring the efficiency of decision making units [J]. European Journal of Operational Research, 1978, 2 (6): 429–444.

    [7] Färe R, Grosskopf S. Intertemporal production frontiers: With Dynamic DEA [M]. Dordrecht: Springer, 1996: 1–8.

    [8] Färe R, Grosskopf S. Network DEA [J]. Socio-economic Planning Sciences, 2000, 34 (1): 35–49.

    [9] Kao C. Network data envelopment analysis: A review [J].European Journal of Operational Research, 2014, 239 (1): 1–16.

    [10] Zheng L, Zhou Z B, Du Y H, et al. Research on allocation efficiency of regional adult education resources using DEA model [J]. Control and Decision, 2020, 35 (3): 721–727 (in Chinese).

    [11] Gong B G, Zhang X Q, Guo D D. Method for hybrid multiple attribute decision-making based on Dempster-Shafer theory and cross efficiency of DEA [J]. Control and Decision, 2016, 31 (5): 943–948 (in Chinese).

    [12] Tseng F M, Chiu Y J, Chen J S. Measuring business performance in the high-tech manufacturing industry: A case study of Taiwan’s large-sized TFT-LCD panel companies [J]. Omega, 2009, 37 (3): 686–697.

    [13] Guan J, Chen K. Measuring the innovation production process: A cross-region empirical study of China’s high-tech innovations [J]. Technovation, 2010, 30 (5): 348–358.

    [14] Liang L, Li Z Q, Cook W D, et al. Data envelopment analysis efficiency in two-stage networks with feedback [J]. IIE Transactions, 2011, 43 (5): 309–322.

    [15] Chen Y, Du J, Sherman H D, et al. DEA model with shared resources and efficiency decomposition [J]. European Journal of Operational Research, 2010, 207 (1): 339–349.

    [16] Cook W D, Hababou M. Sales performance measurement in bank branches [J]. Omega, 2001, 29 (4): 299–307.

    [17] Banker R D, Charnes A, Cooper W W. Some models for estimating technical and scale inefficiencies in data envelopment analysis [J]. Management Science, 1984,30 (9): 1078–1092.

    [18] Chen Y, Cook W D, Li N, et al. Additive efficiency decomposition in two-stage DEA [J]. European Journal of Operational Research, 2009, 196 (3): 1170–1176.

    [19] Lee J, Kim C, Choi G. Exploring data envelopment analysis for measuring collaborated innovation efficiency of small and medium-sized enterprises in Korea [J]. European Journal of Operational Research, 2018, DOI:org/10.1016/j.ejor.2018.08.044.

This Article

ISSN:1001-9952

CN: 31-1012/F

Vol 46, No. 01, Pages 19-33

January 2020

Downloads:6

Share
Article Outline

Knowledge

Abstract

  • 0 Introduction
  • 1 Two-stage DEA model based on shared feedback
  • 2 Empirical implementation and analysis
  • 3 Conclusions
  • References